2021, Volume 21, Issue 1, Pages 73-83
Abstract
Generally, pattern recognition considered a strong challenge in many information processing research fields. The aim of this paper is to propose a highly accurate model for recognizing a handwritten English numeral through efficiently extracting the most valuable features of a certain handwritten numeral or digit. The handwritten English Numerals Recognition Model (HENRM) is proposed in this paper. The features extraction of the proposal based on combining both statistical and structural features of the certain numeral sample image. Mainly, the proposed HENCM has four phases which are image acquisition, image preprocessing, features extraction, and classification. In fact, four feature extraction approaches are utilized in this paper, which are the number of intersection points, the number of open-end points, calculation of density feature, and determining the chain code for each of the English numerals. The latter phase gives a features vector of 26-element size to be fed into the classifier that uses the Multi-class Support Vector Machine (MSVM) for the classification process. The experimental results showed that the proposed HENCM exhibits an average recognition rate equals to 97%.
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